Projection television (PTV) systems and the like typically employ three separate cathode ray tube (CRT) projection units that project an image from each CRT to a common area of a projection screen superimposing the three separate monochromatic color images on one another to provide a single multi-color image. Precise superposition of the three different color images is essential in such a system to avoid degraded resolution and rough, blurred composite images. Thus, the projection units must be adjusted to maintain convergence of the images over the visible surface of the screen. These adjustments are initially made at the factory, but with age, temperature and other environmental conditions, it is often necessary to readjust the convergence in the field in order to maintain the quality of the image on the screen.
Various types of convergence systems, both manual and automated, have been developed to handle the necessary convergence adjustments. Manual convergence systems tend to be labor intensive, tedious and extremely time consuming, requiring hours to complete. Because the manipulator must often have technical knowledge or training sufficient to execute the manual convergence corrections, abilities that ordinary PTV purchasers seldom possess, manual convergence correction is typically accomplished by a skilled technician. In addition, because the manual procedure often requires the use of special test instrument, it may require the inconvenience of removing the PTV from the purchaser's home so that the adjustment can be made at a repair facility.
Although automated convergence systems tend to avoid the disadvantages associated with manual systems, they too have their limitations with respect to accuracy, speed, reliability and expense. One example of an automated convergence system includes the use of a mechanically scanning optical head that samples certain predetermined areas of a projected test pattern. The various mechanical elements and motors of such a system tend to add to its cost and complexity while detracting from system reliability. In addition, convergence accuracy tends to be dependent on motor accuracy and the process still tends to require several minutes to complete.
Another example of an automated convergence system includes the use of a series of photocells positioned at the edges of the projection screen to detect the size of successively projected test patterns. Such systems tend to only converge the very edges of the projection screen where the sensors are located. Often the middle of the screen, which is the most important area to the viewer, tends to be poorly aligned as a result.
In current systems, after convergence adjustment is done by measuring the convergence error between the images from the red, green and blue CRTs sensed in a few points on or around the screen, convergence error of all other points in the red, green and blue images is typically extrapolated by software from these points. Therefore, the more points of measurement, the closer the convergence of the red, green and blue images. Current systems tend to trade off between the number of measurable points and the cost of implementing the number of measurable points.
Accordingly, it would be desirable to provide an inexpensive automated convergence system that more accurately and more reliably achieves convergence over the entire screen without increasing the complexity and cost of the system.